There has been a growing call for reform of the U.S. biomedical community in recent years. As a community, we face a growing list of issues including excessive waste, reproducibility, bias, inadequate training, and the absence of sustainable long-term planning that detract from the overall goal of advancing human health. In response to this debate, biomedical stakeholders have taken positive steps forward to remedy these issues. However, we must continually improve upon these steps to promote the long-term stability of the biomedical enterprise. Given the widespread interest of the scientific community in addressing these issues, there exists a unique opportunity to come together and create a new era of biomedical discovery. The completion of this exciting task requires reflection on our view and management of the system, and what the best route to sustainable change may be. Importantly, a coordinated approach that considers the collective make-up of the biomedical system and how processes and people influence collective output and create value for patients is needed. Here, these three areas and the concepts of systems theory, total quality management, and organizational development and their contribution to the management and effectiveness of biomedical discovery are discussed. Importantly recommendations are made concerning overall management strategy, process efficiency and quality research, administrative tasks, organizational cultural challenges, individual and team development, and funding strategy.

In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

A global approach towards quality management highlights the need for constructing a new body of knowledge that views the field of global quality from a systems perspective. Based on the results of field experiments, and in light of the need to develop new global quality management terminology, the current article presents several key concepts in this field, with emphasis on a systems-oriented rationale and perspective. As such, the article is an important stage in building this body of knowledge, and towards the conceptualization of key variables used in global quality management, from a systems approach that interacts with the fields of international management and strategic management.

The new Common Agricultural Policy of the European Union (EU) (2023-2027) envisages a stronger Agricultural Knowledge and Innovation System (AKIS) that will unite all relevant ac-tors in a co-creation process to ensure knowledge flows within and across Member States. A sig-nificant and increasing body of literature focuses on the strategy and processes of strengthening AKIS. However, there is a relative gap in research related to the role of AKIS in achieving sus-tainability in the farming sector. The purpose of this paper is to provide an overview of the advancements so far of AKIS across the EU when it comes to facilitating the sustainable management of natural resources (SMNR). The paper builds upon a comprehensive review of rel-evant literature during the last 15 years covering all 28 EU countries (including the United King-dom until 2019). We conclude that AKIS architecture in most EU countries lacks a comprehensive structure promoting SMNR. Based on the results we identify countries with relatively high inten-sity of research and development projects linking AKIS to SMNR and where SMNR is better in-tegrated to AKIS. Our results can be of interest to the design of stronger AKIS during the new programming period.

This paper presents a novel approach to power management techniques in energy storage system (ESS) interoperability with the energy power system (EPS) by inserting a SCADA controlled directional protection relay for improving the mode switching sequence. According with latest European standards, all necessary measures for re-synchronization and reconnection for health work of the system must be taken. The proposed approach intends to improve the “interface” between storage systems and electric power grid by using a directional relay which provides interoperability improvement by automatic recognizing and differentiating the switching sequence of the mode of operation from the fault mode. Simulated results from a small scale electrical generating system are provided. The results demonstrate that the proposed power management solution is technically ideal to achieve high quality energy while retaining the stability of the power grid network. The interoperability of PHS systems integrated with the electric power grid usually leads to perturbations in terms of frequency of the generated voltage, and for improving it we designed an automatic treating and re-connecting software module. This work regards the importance of producing electricity from green and renewable energy sources, while retaining the stability and reliability of the power grid. ESS systems are widely used to help balancing of intermittent forms of load demand on the electrical power grid at specific key moments. In this article, the pumped hydro storage (PHS) technologies are proposed as an economical way to utilize smoothly the off-peak energy or surplus energy, by pumping water to a reservoir at a higher level. During peak load periods, the stored water is discharged through the reversible pump-turbine to generate electricity to meet the peak demand controlled by designed SCADA application.

Abstract: Current models in design of urban water management systems and their corresponding infrastructure using centralized designs have commonly failed from the perspective of cost effectiveness and inability to adapt to the future changes. These challenges are driving cities towards using decentralized systems. While there is great consensus on the benefits of decentralization; currently no methods exist which guide decision-makers to define the optimal boundaries of decentralized water systems. A new clustering methodology and tool to decentralize water supply systems (WSS) into small and adaptable units is presented. The tool includes two major components: (i) minimization of the distance from source to consumer by assigning demand to the closest water source, and (ii) maximization of the intra-cluster homogeneity by defining the cluster boundaries such that the variation in population density, land use, socio-economic level, and topography within the cluster is minimized. The methodology and tool are applied to Arua Town in Uganda. Four random cluster scenarios and a centralized system were created and compared with the optimal clustered WSS. It was observed that the operational cost of the four cluster scenarios is up to 13.9 % higher than the optimal, and the centralized system is 26.6% higher than the optimal clustered WSS, consequently verifying the efficacy of the proposed method to determine an optimal cluster boundary for WSS. In addition, optimal homogeneous clusters improve efficiency by encouraging reuse of wastewater and stormwater within a cluster and by minimizing leakage through reduced pressure variations.

Sustainable development (SD) is essential to reduce and mitigate climate change impacts, environmental deterioration and to increase social sustainability. SD is therefore highly relevant for the engineering profession and is today found integrated with the higher education of specific engineering programs. This paper investigated the knowledge of SD for students entering the master’s program in Sustainable Building Information Management (BIM) by comparing levels of knowledge at the start and end of the first course Sustainability, Analyses and Simulations. The level of knowledge of SD was analyzed by classifying students’ conceptions of sustainability using SOLO taxonomy and the spectrum of liminality and the threshold concept. Students written responses to the question “What do you know about sustainability?” and written group project reports were used for analyzing levels of knowledge of SD. Levels of knowledge of SD was classified as pre (pre-liminal or pre-structural); uni-structural, multi-structural, relational and post-stages (extended abstract or post-liminal). In total, 68% of the students entering the master’s program in 2017 and 88% in 2018 showed a pre-structural, uni-structural and multi-structural SD knowledge. In general, few students entering the program showed relational and post-stages of SD knowledge, 32% and 12% of the students in 2017 and 2018, respectively. The students at the post-stage were able to express themselves more individually and creatively compared to previous levels in that they could connect the dimensions of SD to the context of SD of buildings, but also argue why SD of buildings was important and they could also suggest actions or tools for improved SD that engineers should use. Only one group of five (in 2017) showed a post-stage level of knowledge in the group project report. It is likely that the student’s general approach to the work with the reports was to mainly cope with the course requirements which is a sign of surface approach to learning. It, therefore, seems reasonable that future developments of the course should ensure that the students use the scientific literature in their group project reports to make it easier for them to understand the relationship between software use and the connection to green buildings certificate systems and SD of buildings. By making it mandatory to include scientific literature in the reports the students will be encouraged to read and think critically, and deeper, and to put the practical implementation of the software analysis results into a scientific context of SD and BIM.

Background: The ever-growing emergence of antibiotic resistance associated with tuberculosis (TB) has become a global challenge. In 2012, USFDA has given expedited approval to bedaquiline (BDQ), as a new treatment for drug-resistant TB in adults when no other viable options are available. BDQ is a diarylquinoline derivative and exhibits targeted action on mycobacterium tuberculosis but due to poor solubility desired therapeutic action is not achieved. Objective: To develop a QbD-based self-nanoemulsifying drug delivery system of Bedaquiline using various oils, surfactants, and co-surfactants. Methods: The quality target product profile (QTPP) and critical quality attributes (CQAs) were identified with a patient-centric approach which facilitated the selection of critical material attributes (CMAs) during pre-formulation studies and initial risk assessment. Caprylic acid as a lipid, propylene glycol as a surfactant and Transcutol-P as a co-surfactant were selected as CMAs for the formulation of Bedaquiline fumarate SNEDDS. Pseudo-ternary phase diagrams were constructed to determine the optimal ratio of oil and Smix. To optimize the formulation, a Box-Benkhen design (BBD) was used. The optimized formulation (BDQ-F-SNEDSS) was further evaluated for parameters such as droplet size, polydispersity index (PDI), percentage transmittance, dilution studies, stability studies, and cell toxicity through the A549 cell. Results: Optimized BDQ-F-SNEDDS showed well-formed droplets of 98.88±2.1 nm with a zeta potential of 21.16 mV. In vitro, studies showed enhanced drug release with a high degree of stability at 25 ± 2 ºC & 60 ± 5 % and 40 ± 2 ºC & 75± 5 %. Furthermore, BDQ-F-SNEDDS showed promising cell viability in A549 cells indicating BDQ-F-SNEDDS as a safer formulation for oral delivery. Conclusion: Finally, it was concluded that the utilization of a QbD approach in the development of BDQ-F-loaded SNEDDS offers a promising strategy to improve the biopharmaceutical properties of the drug, resulting in potential cost and time savings.

Since the late 1980s the idea of sustainable development has been gaining widespread recognition as a guiding framework for policies on development and the environment. However, the concept of sustainable development has received a number of criticisms, including its over-emphasis on meeting human needs through economic growth, as well as its failure to recognize dynamic human-environment interactions. In response to these shortfalls, the concepts of resilience and adaptive governance have emerged as alternative perspectives for pursuing sustainable development. Resilience in social-ecological systems emphasizes the capacity of coupled human-environment systems to deal with change while continuing to develop. Adaptive governance relies on diverse and nested institutional mechanisms for connecting actors across multiple scales to manage conflicts and uncertainties in ecosystem management processes. However, the ethical dimensions of resilience and adaptive governance have not received enough attention. A promising ethical perspective for guiding policies on human-environment interactions is the philosophy of deep ecology which highlights the need for recognition of the intrinsic values of all living things, as well as the nurturing of ecological and cultural diversity. We argue that an integration of the principles of deep ecology and adaptive governance provides a complementary set of ethical principles and institutional attributes that offers better prospects for pursuing sustainable development in the era of the Anthropocene. The implications of this integrative agenda include: adoption of a holistic conception of dynamic human-environment interactions; recognition of diverse knowledge systems through an anti-reductionist approach to knowledge; promotion of long term sustainability through respect for ecological and cultural diversity; and embracing decentralization and local autonomy. We further illustrate this integrative agenda using the management of protected areas as a case study.

Identifying and assessing risk is one of the most important processes in managing complex systems and requires careful consideration. The need for an effective, efficient approach to risk management is considerably more important for defense industries, because they are exposed to risk already in early stages of development. This paper uses Heterogeneity and Homogeneity analysis between risk factors with Cochran’s Q test and Multidimensional scaling in order to present the complexity of the risk factors relevant to defense SoS, and proposes a methodology for identifying, analyzing and monitoring the risks that they face. Findings from an in-depth analysis of 46 classified defense SoS shows a need to focus on three main risks faced by defense projects: insufficient human resources, changes in the original specifications, and lack of other (non-human) resources. The paper also presents some recommendations for minimizing risk factors in defense SoS.

Identifying and assessing risk is one of the most important processes in managing complex systems and requires careful consideration. The need for an effective, efficient approach to risk management is considerably more important for defense projects based on systems of systems (SoS), because they are exposed to risk already in early stages of development. This paper uses advanced data science tools to present the complexity of the risk factors relevant to defense systems, and proposes a methodology for identifying, analyzing and monitoring the risks that they face. Findings from an in-depth analysis of 46 classified defense projects based on SoS shows a need to focus on three main risks faced by defense projects: uncertainty, the lack of clearly defined goals, and managing a system under constrained conditions. The paper also presents some recommendations for minimizing risk factors in SoS for defense projects.

Clearly policy makers should consider the impacts of any decisions they might make before making them. Science can provide estimates of various economic, ecologic, environmental, and even social impacts of alternative policies, impacts that determine how effective any particular policy will be. These impact estimates can be used to compare and evaluate alternative policies in the search for identifying the best one to implement. Among all scientists providing inputs to policy making processes are analysts who develop and apply models that provide these estimated impacts and, possibly, their probabilities of occurrence. But just producing them is not a guarantee that they will be considered by policy makers. This paper discusses ways scientists, including systems analysts, can effectively contribute to and inform those involved in making water management decisions. Brief descriptions of a variety of past and on-going water management policy making processes illustrate both some successes and failures of science informing policy.

As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

Effective management of water distribution systems requires an integrated approach, but none of the available frameworks for one are in broad use in the water supply industry. Frameworks developed include a management standard of the American Water Works Association and Distribution System Optimization, a methodology for physical, hydraulic, and water quality performance assessment. The intelligent systems framework also offers a pathway to integration, but it lacks a definite structure. The voluntary aspect of adopting innovations within the fragmented and unregulated nature of the water utility industry poses a barrier to adoption of such innovations. Another barrier is the uncoordinated arrangements of water research stakeholders with different incentive structures. Intelligent water systems offer a way to incentive utilities to encourage implementation. They can provide a bottom-up approach where utilities see advantages, as opposed to a top-down approach where they are expected to adopt a method without seeing clear benefits. Research to develop new and improved tools is needed, but the research roadmap should prioritize implementation.

There is increasing demand from stakeholders for tools to support outcome-based approaches in environmental management. For such tools to be useful, understanding user requirements is key. In Scotland, UK, stakeholders were engaged in the development of an Environmental Decision Support System (EDSS) to support the management of land and freshwater resources for multiple policy outcomes. A structured participatory engagement process was employed to determine stakeholder requirements, establish development principles to fulfil these requirements and road-test prototypes. The EDSS specification that emerged from this bottom-up process was spatially-explicit, free at the point of use, and touch and mobile device compatible. This application, which is under development, does not closely resemble most existing published EDSS. We suggest that there is a mismatch between the way scientists typically conceptualise EDSS and the kinds of applications that are likely to be useful to decision-makers on the ground. Interactive mobile and web-based geospatial information services have become ubiquitous in our daily lives, but their importance is not reflected in current literature on EDSS. The current focus in environmental management on adaptive, stakeholder-centred strategies based on outcomes offers an opportunity to make better use of these new technologies to aid decision-making processes.

This study proposes a framework to integrate Sustainability within Management Systems Standards and subsequently implement and disclose Sustainable Development goals and results. Moreover, it investigates the SD goals (SDGs) and results (SDRs) that Portuguese organizations with Integrated Management Systems (IMSs) disclose to their interest parties. The study, supported by content analysis, highlights that the four more frequently disclosed SDGs are “life on land“(50.0%), “industry, innovation, and infrastructure” (47.1%), “responsible consumption and production” (47.1%), and “partnerships for the goals” (47.1%). Conversely, the four SDRs most frequently disclosed are “employment” (82.4%), “economic performance” (79.4%), “anti-corruption” (64.7%), and “occupational health and safety” (61.8%). Hence, SDGs disclosure emphasizes the environmental dimension, while SDRs disclosure highlights the social dimension (economic dimension present in both SDGs and SDRs). Finally, the disclosure of SDGs and SDRs in institutional reports presents a positive and strong correlation that is statistically significant. Overall, the contributions of this research are twofold. First, it supports the integration of the SDGs within organizations, and second, it stimulates the demonstration of their impacts on the SDGs (the SDRs).

The implementation of management systems has become a strategic advantage in achieving business goals, especially in industrial organizations, but the implementation of social responsibility requirements is especially ethical. Large multinational companies have long been developing and, in particular, implementing their own codes of conduct, which include their suppliers, to demonstrate their commitment to Corporate Social Responsibility (CSR). The compliance with CSR requirements from stakeholders is thus extended or intertwined with requirements in established management systems (MSs). The objectives of the study were to (1) analyze the different approaches to CSR in internationally recognized cross-industry and industry-specific standards and codes in different industries; (2) select the most appropriate framework for assessing the degree of applicability of CSR in the selected management systems and develop a methodology for its assessment; (3) apply the proposed methodology (referred to as SRIMS) in the selected areas: automotive industry, research organization, and metallurgical industry; (4) through ANOVA, validate its use for assessing the overall level of CSR applicability in an organization's established management systems. The application of the Bonferroni method confirmed the hypotheses that the developed SRIMS model is a sufficiently appropriate tool for assessing the overall level of applicability of CSR requirements to established MSs.

This paper directly addresses a long-standing issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply, and reliably whether they can deliver their intended performance before expending significant time, effort and money on detailed design and implementation. We describe ∆QSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The distinctive feature of outcome diagrams is that they capture the essential observational properties of the system, independent of the details of system structure and behaviour. The ∆QSD paradigm derives bounds on performance expressed as probability distributions encompassing all possible executions of the system. The ∆QSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way, so that approximate results can be obtained quickly and subsequently refined. ∆QSD has been successfully used by Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings worth billions of US dollars. The paper outlines the ∆QSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain. ∆QSD has enabled challenging throughput targets to be met for a globally distributed blockchain operating on the public internet.

This article considers the concepts of sustainability and sustainable development in relation to disaster risk reduction and climate change adaptation. We conceptualize sustainability from a social systemic perspective, that is, from a perspective that encompasses the multiple functionalities of a social system and their interrelationships in particular environmental contexts. The systems perspective is applied in our consideration and analysis of disaster risk reduction (DRR), climate change adaptation (CCA), and sustainable development (SD). Section 1 introduces briefly sustainability and sustainable development, followed by a brief presentation of the theory of complex social systems (Section 2). The theory conceptualizes interdependent subsystems, their multiple functionalities, and the agential and systemic responses to internal and external stressors on a social system. Section 3 considers disaster risk reduction (DRR) and climate change adaptation (CCA), emerging in response to one or more systemic stressors. It illustrates these with disaster risk reduction in the cases of food and chemical security regulation in the EU. CCA is illustrated by initiatives and developments on the island of Gotland, Sweden and in the Gothenburg Metropolitan area, which go beyond a limited CCA perspective, taking into account long-term sustainability issues. Section 4 discusses the limitations of DRR and CCA, not only their technical limitations but economic, socio-cultural, and political limitations, as informed from a sustainability perspective. It is argued that DRRs are only partial subsystems and must be considered and assessed in the context of a more encompassing systemic perspective. Part of the discussion is focused on the distinction between sustainable and non-sustainable DRRs and CCAs. Section 5 presents a few concluding remarks about the importance of a systemic perspective in analyzing DRR and CCA as well as other similar subsystems in terms of sustainable development.

Computer systems that operate in remote locations such as satellites, remote weather stations and autonomous robots are highly limited in the availability of energy for their operation. This work aims to employ artificial intelligence algorithms in energy management in order to obtain the maximum energy yield and the prediction of energy availability to the system. The work presents the main types of algorithms used in artificial intelligence and presents the creation of a prototype that will operate as a low power system powered by batteries and a small solar plate, the prototype will perform inferences on the LSTM neural network algorithm in order to predict the future availability of energy, consequently the management system will carry out the energy distribution in order to obtain the maximum operation of the prototype without total discharge of the batteries. So that the artificial intelligence system could be embedded in the prototype, the TensorFlow Lite framework was used, which allows the inference to be carried out in devices with low consumption and limited processing power.

Risk assessment management have been a hot topic for the researchers since a very long time. Software risk management is an important part of project management as it contains the identification, analysis, estimation and monitoring of different risks present in the system. This helps developers in decision making while assessing the problems that could arise in the software systems. Risk management is very complex in large scale system as these systems have very complex development. The paper describes risk management techniques for large scale system. Furthermore we have provided a detailed comparative analysis of these techniques with commonly identified risks in software systems and have provided a systematic order for risk management process to ensure risk mitigation.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

Quantifying different stocking rates effects in three study areas placed at Mediterranean forest (Cuenca, Spain) was conducted by applying a multiparametric soil quality index, SQI, developed in undisturbed forest soils (> 40 years). The main objective is to advance in the development and application of multiparametric indexes which allow for soil condition assessment. For it, is analyse the effectiveness of a multiparametric soil quality index (SQI) as an indicator livestock impacts on the soil in mediterranean forests. Control areas without stocking rates were compared in forest stands with different age (Thicket forest stand, < 30 years; High polewood forest stand, 30-60 years; Old growth forest stand, > 60 years); with various intensity grazing areas (permanent livestock passage: inactive sheepfold for 2-3 years, and active sheepfold; intermittent livestock passage: bare soil, pine stand, scrubland). The multiparametric soil quality index (SQI) applied sensitive to changes in forest ecosystems depending of different stocking rates. However, for even more preci-sion to assess cattle alteration, multiparameter index recalibrated, creating a new one; Soil Status Index by Livestock (SSIL). Correlation between quality ranges obtained with both indexes in dif-ferent study areas suggest that SSIL can be considered a livestock impact reference indicator in Mediterranean forest soils.

Network slicing is a promising technology for 5G networks in which operators can sell customized services to different tenants at various prices and Quality of Services (QoS) demands. Thus, the latest 4th Generation (4G) and upcoming 5th Generation (5G) mobile technologies are expected to offer massive connectivity and management of high volume of data traffic in the presence of immense interferences from mobile networks of IoT devices. Further, it will face challenges of congestion and overload of data traffic due to the humongous number of IoT devices. Nevertheless, these devices are likely to demand high throughput, low latency, and high level of reliability especially for critical real-time applications such as in Vehicular Communication System (VCS). To address these issues in 5G mobile networks, this paper proposes a Slice Allocation Management (SAM) Model based on the critical services of smart systems such as VCS to satisfy QoS demands. The proposed model aims at providing dedicated slices on the basis of service requirements such as expected throughput and latency for VCS. To ensure such performance and provide data traffic priorities of IoT devices in the uplink of Relay Nodes (RNs) cells in the 5G mobile networks, we have sliced the Radio Access Networks (RAN), along with the assignment of the nearest Mobile Edge Computing (MEC) with isolated slices based on the priorities for each IoT nodes to reduce latency level. The proposed model was simulated and validated using the OPNET simulator. The results obtained demonstrate that SAM Model is able to achieve improvement of end to end delays and uplink throughputs of the networks in high-density networks of IoT devices.

The increasing environmental and human health concerns associated with medical waste have led to the development of environmental biotechnology as a means of mitigating its negative effects. This review paper discusses the various types of medical waste generated in healthcare facilities, as well as the potential environmental and health impacts of improper handling and disposal. It also provides an overview of the different environmental biotechnologies that have been developed to treat medical waste, including bioreactor systems, microbial treatment, and composting. The benefits and drawbacks of these technologies are also discussed. Bioremediation is a promising environmental biotechnology that can be used to treat medical waste contaminated with hazardous chemicals. Case studies from around the world demonstrate the successful application of environmental biotechnologies to medical waste management. These case studies highlight the challenges encountered, the methods used, and the results obtained. The findings of this review paper highlight the need for a comprehensive strategy for managing medical waste that includes environmental biotechnologies. Further research and development are needed to improve the effectiveness, efficiency, and sustainability of medical waste management.

This paper presents a new information technology platform specially tailored for infrastructure asset management of urban water systems operated by water utilities of lower digital maturity level, developed in the scope of DECIdE research project. This platform aims at the integration of different data from the water utilities with several information systems and the assessment of the system performance, in terms of water losses, energy efficiency and quality of service by using developed tools (i.e., water and energy balances and key performance indicators). This platform was tested with data from five small to medium size Portuguese water utilities with different maturity levels in terms of technological and human resources. Obtained results are very promising since the platform allows to assess the systems performance periodically which constitute an important part of the infrastructure asset management for small and medium-sized water utilities

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry and science related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, rational design of combination therapies and the global response to the Covid19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human focused, systems-based approach to drug discovery and research.

Research focused on understanding wider systemic factors driving behavioral change is limited with a dominant focus on the role of individual farmer and psychosocial factors for farming practice change, including reducing fertilizer application in agriculture. Adopting a wider systems perspective, the current study examines change and the role that supporting services have on fertilizer application rate change. A total of 238 sugarcane growers completed surveys reporting on changes in fertilizer application along with factors that may explain behavior change. Logistic regressions and negative binomial count-data regressions were used to examine whether farmers had changed fertilizer application rates and if they had, how long ago they made the change, and to explore the impact of individual and system factors in influencing change. Approximately one in three sugarcane growers surveyed (37%) had changed the method they used to calculate fertilizer application rates for the cane land they owned/managed at some point. Logistic regression results indicated growers were less likely to change the basis for their fertilizer calculation if they regarded maintaining good relationships with other local growers as being extremely important, they had another source of off-farm income, and if they had not attended a government-funded fertilizer management workshop in the five years preceding the survey. Similar drivers promoted early adoption of fertilizer practice change; namely, regarding family traditions and heritage as being unimportant, having sole decision-making authority on farming activities and having attended up to 5 workshops in the five years prior to completing the survey. Results demonstrated the influence of government-funded services to support practice change.

It is necessary to manage the power consumption and use fault-tolerance techniques to meet high reliability in Real-Time Embedded Systems, . Hence, in recent years, this is the main reason for providing an ideal approach to decrease power consumption through the worst-case behavior of instantaneous power management (peak-power management), while observing the Thermal Design Power (TDP). Although the multi-core chips in real-time embedded systems provide great opportunity for implementation of Two-phase Triple modular redundancy to meet high reliability, the energy overhead management of concurrently executing tasks is one of the challenges facing designers. In this article, we propose a scheme named Permanent Fault Aware, Two-Phase Peak-Power Management (PFA-TP3M), for scheduling real-time tasks in multi-core systems to eliminate peak power overlap from concurrent running tasks to maintain peak power consumption at the chip TDP. and through mapping different parts of each task on separate cores, prevents consecutive conflicts on cores, which have faced permanent faults to design the fault-tolerant system. Our simulation results show that our proposed scheme provides up to 700x fault tolerance increase (630x on average) compared to the state-of-the-art power management algorithm, an achievement costs up to 14%. (average 11%) increase the length of the task graph compared to it.

The coordination planning between land use and transportation system is an important premise of solving urban transportation problems and realizing land use integration. This study investigates the interactive and feedback relationship between land use and transportation system from the perspective of access management. By integrating the land use and traffic data from Las Vegas Metropolitan area with the system dynamics model, the causal relationship and causal loop diagrams (CLDs) are introduced to analyze the cause-and-effect relationship and quantitative relationship between the factors of the combined system of land use and transportation, and then sub-models partition and system simulation are performed. The systems dynamics model is established by analyzing the relationship between a series of access management techniques, traffic characteristics, and land use features. The results show that system dynamics model can be used as an effective alternative to model the symbiosis relationship of land use and transportation system for urban planning and construction.

This paper discusses a MEMS switch that can be fabricated using low temperature (<100^oC) deposition and patterning techniques suitable for the back-end-of-the-line integration with CMOS. The resulting cross-bar switches can be used for electrostatic discharge protection, FPGA implementation, chip security assessment and lock-down, and circuit block power management. We discuss platinum and iron switch with turn-on voltages of ~ 1.8 V. In the case of the iron switches, we also show that they can be magnetized to have “memory” and stay on when turned on. Platinum switch cycling of up to 1000 times did not show any changes in their turn-on voltage and their contact resistance was unchanged. The 10-100 nm switch airgaps were formed using low temperature sputtered sacrificial polysilicon and XeF₂ etching. XeF₂ does not attack any of the metals used in CMOS enabling fabrication of cross-bar switches with any of these metals. Once activated, it takes ∿ 6 to mechanically turn on the switch that can be decreased to ~1 ns by optimizing the device structure. Interestingly, the nm-scale gaps can be used as spark gap as a fast plasma switch to discharge first followed by the activation of the MEMS switch.

The escalating environmental challenges that stem from urban residential waste in densely populated areas have become increasingly prominent in recent times. As a result, the waste management sector has experienced significant growth nationwide. However, due to the predominantly manual nature of these processes, their effectiveness falls short of meeting the current demand. Addressing these issues requires process enhancement. In this context, Industry 4.0, particularly the concept of Digital Twins, emerges as a potential avenue for refining processes. To tackle both current and future industry challenges, it is imperative to create a comprehensive methodology capable of generating effective solutions within the waste management sector. This paper provides an explanation of digital twins, outlines the methodological framework comprising an architectural structure and layered model, and details a series of sequential methodical stages for implementing digital twins based on the specific scope determined by the end-user. A noteworthy aspect is the transformative potential of digital twins in enhancing efficiency and precision within waste management practices. By providing a virtual real-time representation of the waste management system, digital twins empower simulations and experimentation to fine-tune processes. Furthermore, they facilitate informed decision-making by offering a detailed visualization of the complete system, simplifying the identification of challenges and opportunities for improvement. that this article derives from the presentation titled "Digital Twin application methodology for the improvement of production and service systems. Application to waste management processes" at the "Sustainable Smart Cities and Territories International Conference" held in the city of Manizales, Colombia, from 21st to 23rd June 2023.

The analysis of the land use dynamics of the Lac Télé Community Reserve (RCLT) using Landsat Thematic Mapper (TM) and (Enhance Thematic Mapper) ETM+ images highlight significant changes in the vegetation cover from 1980 to 2000 and 2018. Thus, the rate of forest area decreased by 21.41% for the entire LTCR in favor of savannahs which increased by 15.23%. The conversion of this forest area to savannah due to the practice of slash and burn agriculture facilitates the opening up of the forest area and contributes to greatly degrading the spawning grounds of fish species from the Likouala aux herbes river. For the mapping of fishing activity in general and the ecological characterization of the 151 identified spawning grounds in particular; the respective mean values of the physical and chemical water parameters; temperature (28.13°C), pH (4.23) and depth (3.34) did not vary significantly from one selected village to another between July and September 2019. The fish diversity unregistered during the study, in the 07 pilot villages would be due to the diversity of the microhabitats noted in the villages of the LTCR, especially from the villages; Botongo, Mossengue and Bouanela where the indices of ichthyological diversity were the highest.

This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach

Background: Cutaneous malignant melanoma (CMM) is one of the most aggressive types of skin cancer. Currently, innovative approaches such as target therapies and immunotherapies have been introduced in clinical practice for the treatment of metastatic CMM. Data of clinical trials and real life studies that evaluate the outcomes of these therapeutic associations are necessary to establish their clinical utility. The aim of this study is to investigate the types of oncological treatments employed in the real-life clinical management of patients with advanced CMM in several Italian centers which are part of the Clinical National Melanoma Registry (CNMR), and the oncological outcomes obtained. Methods: CNMR collects data of patients with a histologically confirmed diagnosis of primary CMM treated in one of the 38 Italian institutions (hospitals, research institutes, etc.) participating in the network. Melanoma-specific survival and Overall survival were calculated. Kaplan-Meier curves and medians of OS and 95% CI are presented overall and by immunotherapy and target treatments. The Log-rank test compared curves by treatments. Multivariate Cox regression models were used to estimate the hazard ratios adjusting for confounders and other prognostic factors. Results: The median follow-up time was 36 months (range 1.2-185.1). 787 CMM were included in the analysis with completed information about therapies.Global immunotherapy showed a significant improved survival compared with all other therapies (p=0.001). 75% was the highest reduction of death reached by nivolumab/pembrolizumab immunotherapy (anti-PD1 HR=0.25 95% CI 0.14-0.42), globally immunotherapy was significantly associated with improved survival, either for anti-CTL A4 monotherapy or combined with anti-PD1 (HR=0.47;95% CI 0.33-0.66 and HR=0.26; 95% CI 0.15-0.46, respectively). Conclusions: The nivolumab/pembrolizumab and the combination of ipilimumab can be considered the best therapy to improve survival in a real-world-population. The CNMR can complement clinical registries with the intent of improving cancer management and standardizing cancer treatment.

In this study, a multiobjective, multiperiod, global optimization for design, sizing and dispatch of an islanded, hybrid microgrid was performed using a model built in MATLAB. The system was simulated over one year for sizing and over one day for dispatch, both using hourly time steps. The model minimized lifecycle levelized costs, emissions, lost load and dumped power while maximizing penetration of clean, renewable sources in the microgrid. This found optimal capacities of the renewable, energy storage and backup generation components which provide the best combination of affordability, sustainability, reliability and efficiency. After experimenting with several global solvers, it was determined that particle swarm optimization is most well-suited to solving the sizing optimization problem. The PV-wind microgrid using Li-ion batteries along with diesel engines was found to perform best among all the combinations considered. It was found that in spite of including additional objectives, monetary costs are the primary driver while allocating generation capacity between different renewable sources like wind versus solar PV. Furthermore, the sizing of PV, wind and battery storage depends strongly on the rating of the standby distributed generator, mainly due to reliability consideration. Generating Pareto-optimal sets revealed interesting relationships between different input variables (i.e. PV, wind and battery capacities) as well as trade-offs that arise while pursuing different objectives. Pursuing cost-minimization alone may lead to sub-optimal outcomes in terms of environmental impact, reliability and excess energy production. A sensitivity analysis was also conducted to understand the effects of various parameters like fuel price and energy storage costs on the optimal system's design and operation. Such accurate sizing programs help reduce the extent of oversizing of sub-systems during the design and planning stage, which is usually needed to achieve high reliability with distributed and decentralized energy systems like off-grid microgrids. This reduces the upfront capital investment needed to build the system, making clean electricity access affordable in the short term. The economic-environmental dispatch produced day-ahead scheduling strategies to meet the above mentioned objectives. The system was found to be relatively robust to short-term uncertainties and disturbances in renewable generation and load, although this does cause sub-optimal performance due to increased reliance on fossil fuels. It was found that dispatching of the batteries and backup generators is most critical in minimizing impacts of such events. However, the response to longer-term disturbances still remains to be assessed. The study also includes a comprehensive literature review of tools available for microgrid design as well as different optimization algorithms that have been used to solve microgrid sizing, dispatch and scheduling problems. Additionally, an overview is provided of various control strategies that can be used to improve robustness and resiliency of microgrids.

The sustainability of agriculture depends as much on the natural resources required for production as it does on the stakeholders that manage those resources. It is thus essential to understand the variables that influence the decision-making process of agricultural stakeholders to design educational programs, interventions, and policies geared towards their specific needs, a required step to enhance agricultural sustainability. We examined the perceptions, experiences, and priorities that influence management decisions of five major groups of agricultural stakeholders (conventional small grain producers, organic small grain producers, organic vegetable producers, extension agents and agro-industry crop consultants, and researchers) across the Montana, United States. Results revealed that while stakeholder groups have distinct perceptions, experiences, and priorities, there were similarities across groups. Specifically, organic vegetable and organic small grain producers showed similar responses that were, in turn, divergent of conventional producers, researchers, and crop consultants. Conventional small grain producers and researchers showed overlapping response patterns while crop consultants formed an isolated group. Our results reinforce the need for agricultural education and programs that address unique and shared experiences, priorities, and concerns of multiple stakeholder groups. This study endorses the call for a paradigm shift from the traditional top-down agricultural extension model to one that accounts for participants’ socio-ecological contexts to facilitate the adoption of sustainable agricultural systems that support environmental and human wellbeing.

This paper is focused on the analysis of methods used to assess effectiveness and efficiency of the occupational safety and health management system (OSHMS) in a company. It reviews current Russian and international occupational safety and health standards. The paper details the method outlined in International Standard GOST 12.0.230.3-2016 “Occupational safety standards system. Management systems for occupational safety. Evaluation of effectiveness and efficiency” based on the data collected during the external audit of OSHMS function efficiency in a Russian mining company. Effectiveness and efficiency indicators were determined, and a generalized desirability coefficient was calculated. Weaknesses of the method proposed in GOST 12.0.230.3-2016 were identified and mathematical and linguistic solutions were proposed to improve it. Harrington function was used to determine a numerical and linguistic score. The performed calculation demonstrated that the company needed strategic management decisions to improve the current situation. Practical approaches were offered to enhance the company's systems-based occupational safety and health performance..

The 2030 global agenda of the United Nations emphasizes peace, human rights, gender equality, partnership, and women's empowerment. It balances the social, economic, and environmental dimensions of sustainable development. The SDG framework was designed to be integrated and indivisible, making the interconnections of 17 Goals and 169 Sustainable Development Goals Targets (SDTs) more complex that need extensive and intensive investigative research. Recent studies have increased on the interrelationships of SDGs, but none of these have focused on integrating Climate Change (CC), Sustainable Forest Management (SFM), and Information and Communication Technology (ICT), also known as CSI Nexus. This study aims to investigate and identify the 169 SDTs linked to CC, SFM, and ICT and assesses the significance of the relationships between these variables. The alignment of SDTs to CSI Nexus was identified through clustering and mapping techniques. The result argued that 56 SDTs are directly connected within CC+SFM+ICT, 16 within CC+SFM, one within SFM+ICT, and 51 within ICT+CC. Our result suggests that CC is significantly associated with SFM; however, ICT has no significant association with CC and SFM. It further asserts that the ICT and SFM have minimal influence on the SDG 2030 framework. The proposed CSI Nexus and SDTs Integration Framework was a science-informed guide for priority-setting, policy coherence, and decision-making supporting the 2030 Sustainable Development Goals. This study does not include interactions, network analysis and visualization, simulations, and modeling between SDG targets.

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers the practical application of the theoretical principles of converged security presented in the publication to a functional sample, deployed and tested in practical conditions of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the context of resilience assessment is defined. In the beginning, the general principles of security/ safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment method is presented for better understanding. Subsequently, PSIM and SIEM systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes.

Forest management aims to preserve ecosystem integrity, maintaining resilience thresholds. For this, conservation and invasion patterns must be determined in managed landscapes. The objective was to identify proxies (landscape and environmental patterns) that allowed the plant species invasion and define thresholds of human impacts to improve management practices. We also want to identify indicator species for the different kinds of impacts and environments. 165 plots were measured in Nothofagus antarctica forests and associated open-lands (dry and wet grasslands) in Tierra del Fuego (Argentina). We found differences in the studied variables across landscape and among different uses and impact types. Human impacts influenced over land types, emphasizing the importance of managing intensities. Indicator plant species allowed to identify potential ecological thresholds related to human impacts and the establishment of species linked to ecological and economic degradation, e.g. Bolax gummifera and Azorella trifurcata were associated with high grazing pressure in grasslands and fire in forested areas, while Rumex acetosella and Achillea millefolium, typically associated with forested areas, were related to high harvesting pressure and fire impacts. These findings contribute to understanding the long-term effects of some impacts, and allow us to define variables of monitoring and indicator species for each impact type.

The administration and management of urban transport has become an urgent need, in the context of the increasing congestion problems and the environmental and economic damage, resulting from rapid urban expansion witnessed by the Algerian cities in recent times, in order to achieve prosperity and quality of urban life. It became necessary to use the tools of modern technology, which administrative and technical departments are seeking to use primarily to support ways of decision-making and actions required. Boosted by high capabilities in the statistical data and other information is stored, and coordination and tabulation, with the possibility of installation and analysis by operations. Especially against the inability of traditional methods, which have become less able to cope with the changes that led to the urban dynamic currently experienced by urban communities in our country. This research aims to employ geographic information systems in the administration and management of the urban transport network, facing both the population and the local administration of the city of Batna. Which was chosen as a model for major Algerian cities, which is suffering more than ever of transportation bottlenecks, as well as the unintended damage for these networks and other networks. Hence the importance of the subject of our research, which aims to use GIS MapInfo systems as a means to snipe (Data Capture) and storage (Data Storage) and address (Data Processing) and directed by spatial information (Spatial Data) and link them to the descriptive information (Attribute Data). To develop a modern digital data and charts the base of each of these elements of the urban service lines equipment and mechanisms, ... etc. As a tool available to those creating and carrying out urban spaces, as well as decision-makers. It was concluded that the possibilities offered by this system would facilitate the processes of monitoring and management in order to ensure the quality of the service.

Existing research lacks studies that explore the impact of government fiscal support on the high-quality development of regional economy and society from the perspective of livelihood-based infrastructure construction. The investigation in this paper explores the economic and social effects of government fiscal support behavior from the perspective of livelihood infrastructure construction. On the basis of the TOPSIS-Entropy Method, this paper constructs an evaluation indicator that closely conforms to the denotation and requirements of high-quality development and then carries out empirical regressions in combination with government expenditure on people's livelihood to verify the theoretical hypothesis, whose transmission mechanism and moderating effect are further empirically tested. The results reveal that increasing government expenditure on people's livelihoods can effectively improve regional high-quality development, but the marginal improvement degree varies with different periods and locations. The mechanism test proves that the regional consumption level plays a significant part in the mediating effect, accounting for 24.27% of the total effect. Urbanization is also the critical impetus for regional high-quality development and positively moderates the promotion effect of people's livelihood fiscal expenditure. Our study presents a theoretical basis and policy inspiration for improving residents' quality of life and welfare level and improving people's livelihood in the midst of high-quality economic development.

The present era of the Internet of Things (IoT) having intelligent functionalities in solving problems pertaining to realtime mission-critical systems has brought an immense revolution in diverse fields including healthcare and navigation systems. However, to the best of our knowledge, the potential of IoT has not been fully exploited yet in the field of the energy sector. We argue that there is an immense need to shift the traditional mission-critical electric power system architecture to IoT-based fully orchestrated architecture in order to increase efficiency, as billions of investment is reserved for the energy sector globally. Since network orchestration deals with auomating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the architecture. The mission-critical systems with urgent task execution often suffer from issues of missing task deadlines. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of nonrenewable energy resources and maximizing the use of renewable energy resources. Moreover, major components of IoT task orchestration such as task mapping and task scheduling are also enhanced using NLP and PSO optimization modules. The proposed task scheduling algorithm incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid mission-critical system. Furthermore, the comparison with state-of-the-art scheduling algorithms showed that the task starvation rate is reduced to 16% and 12% when compared with RR and FEF algorithms, respectively.

Despite ample warning and significant lead time, global society has not acted sufficiently to combat climate change, and global carbon dioxide emissions are now about 60% higher today than in 1990 [1]. This paper uses marine fisheries as an analog model to compare with the many similar management issues faced with global climate change. Both marine fisheries and global climate change are complex systems that have similar properties that make management very difficult. A simple fishery prototype model is used to educate citizens about similar issues of dynamic complexity found in climate change models. We highlight how our slow reaction time to global climate change can be thought of as a mismatch between our cognitive abilities and the nature of global collective problems. Understanding the history and system structure of marine fisheries can provide insight into managing global climate change. We suggest ways to improve communication between science and citizens for updating mental models and changing climate policy.

This paper proposes a Case-Based Reasoning (CBR) system to contribute to reinforce the sustainable performance of an environmental management system. The CBR system aims to support the decision-making process to select environmental management actions aimed at reducing risky trends of the environmental state of a region. The CBR system takes advantage of a set of situation-solution pairs called cases, which are stored in a memory and then retrieved as candidates to solve new problems. Situations in this work are represented by a set of risky trends of the following key environmental variables: CO2 emissions, Air-Quality, Loss of Vegetation Cover, Water Availability, and Solid Waste, whose combination damage the environmental state quality of a region. Meanwhile, solutions are represented by a set of environmental management actions. Similar situations to a given current situation are retrieved from the memory of cases and then their solutions are combined, through an adaptation mechanism, until the solution of the current problem is obtained. We used risky trends derived from real data related to the environmental states of a Mexican region to test the proposed CBR system. The results obtained provided insights into the potential of CBR systems to support the decision-making process to select environmental management actions aimed at reducing risky trends of current environmental states.

Background:This study evaluated the effect of low-level laser therapy on postoperative pain and wound healing in children undergoing primary molar extractions.Methods:40children,6-10years of age, systemically healthy, and had atraumatic extraction indications of bilateral primary molar teeth were included in the study. First session randomly selected tooth was extracted under local anesthesia. In the control group, only the clot formation on the socket was observed and photographed. Other group extraction's performed 2weeks later. The LLLT group treated with980nm wavelength Doctor Smile Wiser diode laser and photographed. Non-epithelialized surface measurements performed with the ImageJ program. Pain assessment was performed with Wong-Baker Pain Scale. Statistical analyzes were performed with SPSS software.Results: There was no statistically significant difference in Wong-Baker values(p&gt;0.05). The laser group had more '0' values on days 1 and7; same values as the control group were recorded on other days. In soft tissue healing 3rd day evaluations, non-epithelialized surface of laser socket was found to be smaller than control group, and measurement results were found to be statistically significant(p&lt;0.05). The other days' measurement results were not statistically significant(p&gt;0.05).Conclusions:Although LLLT not found to be very effective in reducing postoperative discomfort after extraction of primary molars, it provides better wound healing in extraction sockets.

Studies comparing provider and patient views and experiences of self-management within primary healthcare are particularly scarce in disadvantaged settings. In this qualitative study, patient and provider perceptions of self-management were investigated in five socio-economically disadvantaged communities in Stockholm. Twelve individual interviews and three group interviews were conducted. Semi-structured interview guides included questions on perceptions of diabetes diagnosis, diabetes care services available at primary health care centers, patient and provider interactions, and self-management support. Data was analysed using thematic analysis. Two overarching themes were identified. These were characterized by inherent dilemmas representing confusions and conflicts that patients and providers experienced in their daily life or practice respectively: adopting and maintaining new routines through practical and appropriate lifestyle choices (patients); and balancing expectations and pre-conceptions of self-management (providers). Patients found it difficult to tailor information and lifestyle advice to fit their daily life. Healthcare providers recognized that patients were in need of support to change behavior, but saw themselves as inadequately equipped to deal with the different cultural and social aspects of self-management. This study highlights patient and provider dilemmas that influence the interaction and collaboration between patients and providers with respect to communication and uptake of self-management advice.

Over the years, Artificial Intelligence (AI) has seen a steady progress in development and evolution that can aid many sectors. Today, AI plays an essential role in the Fourth Industrial Revolution, and it is making its way into the military sector as well. Many countries are actively looking into AI military technology. Some of these innovations include image recognition, text analysis, Self-driving vehicles (SDV), gaming, robotic process automation (RPA) & Robotic and Autonomous Systems (RAS), and Autonomous Weapons Systems (AWS). In this research, we discuss the advantages of each innovation mentioned and analyze the many different cybersecurity threats that awaken due to military artificial intelligence systems. We then discuss the open research areas to better improve the current state of military artificial intelligence applications in terms of ethics, system security, proper strategy plan, accepted responsibility matrix, and introduction of relevant laws.

Urban areas face daunting economic challenges that have increased in scope in recent years. At the same time, cities provide for opportunities for growth and value creation. The interplay of these challenges and opportunities represents the area of intervention for policymakers and researchers. However, the traditional approach to urban policy, deriving from the managerial and economic policy theories, shows difficulties mainly related to the capacity to counterbalance the interests and expectations of a multitude of stakeholders participating to the value co-creation process and to bring all different components to synergically contribute to the sustainability of the system. The Author proposes an innovative approach to urban public policy, by adopting the theoretical corpus of the Viable Systems Approach ("VSA"). Urban planning will be particularly analysed, as systemic components of urban areas, with the aim to investigate the decision-making processes for an integrated, efficient, effective and sustainable management of the territory as a multidimensional, multisectoral and multi-stakeholder entity. By recovering different settings, the VSA intends the co-creation of value as the capacity of the viable systems to increase its surviving chances in its own context. The value derives, first of all, from the strategic decisions aimed at finding both dyadic and context consonance. Through its strategic decisions, the Governing Body of the territory develops a specific system as the overall synthesis of all possible systems organised within a specific area, identifying pattern of development agreed by the several stakeholders of the territory. In the dynamics of value co-creation in systemic multi-subjective organizations, where the level of complexity is particularly significant, the research for consonance among different stakeholders is particular important in order to achieve the most value for the territory. In this respect, the VSA may constitute a valid tool, helping to find the necessary capacities to imagine evolutionary paths towards new competencies.

We argue for reconsidering leadership development based on systems design principles. A primary advantage of systems thinking is that it encourages holistic approaches to development and avoids episodic program-based training and piecemeal thinking. The latter approaches are both limited and limiting yet tend to be the preferred approach to leadership development in organizations. Systems approaches to development offer numerous advantages both conceptually and pragmatically, especially through the incorporation of ongoing feedback cycles. Core practices that define a leadership development system are presented and implications are discussed.

A conventional electrical grid mostly depends on the electrical power generated from fossil fuels. However, the pollutants from fossil fuels are the key factors for adverse climate change. Most of the developed countries of the world have already recognized the fact that the energy mix requires to be diversified by incorporating renewable energy. This is especially relevant for many of the member countries of the Organization of Islamic Cooperation (OIC), consisting of 57 countries, whose abundance of fossil fuel reserve indicates that much of their electric power is still generated from fossil fuels. In order to integrate renewable energy sources into the hybrid energy mix, an existing conventional grid needs to undergo drastic changes. Alongside this, the population boom in the OIC member countries has caused higher demand for a steady supply of electricity that the conventional grids have long been struggling to cope with. With a view to solving this multifaceted problem, incorporation of the smart power management schemes is indispensable using a smart electrical grid, where information and communications technology is integrated into its major building blocks. This allows advanced applications of a grid, such as the formation of micro-grids, demand-side management, energy storage, high-tech power electronic converters, etc. As the smart grids are being adopted by many developed countries, it is high time for the OIC member countries to pay due attention to this development, if they have not already done so. This paper explains, with special focus on the OIC member countries, the various smart power management technologies, their operations and applications, and the benefits and challenges. Then it goes on to carry out the Strength-Weakness-Opportunity-Threat with Analytical-Heuristic-Procedure (SWOT-AHP) analysis to evaluate its feasibility of incorporation and the underlying strategies appropriate for its implementation. Furthermore, a Hybrid Multi-Criteria-Decision-Making (MCDM) analysis is performed to evaluate the sequence of the emphasis that should be given on each of the technologies from those available for the smart power management initiative. Finally, the study reinforces the stance by drawing parallels from the UN Sustainable Development Goals (SDG) and highlights the importance of the smart grid in line with the global vision of SDG. This paper aims at assisting the decision-makers in implementing smart power management schemes in the OIC member countries, in particular, and other countries of the world, in general.

Deadlock is a real problem that can cause expensive, time-dependent hang-ups or failures in distributed computing systems. Some studies have been conducted with deadlock detection and resolution in generalized request computing models, most of these studies are based on the diffusing computation scheme, where forward and backward propagated probe and reply messages respectively are diffused between dependent processes. The replies carry the dependency information between processes for the initiator. Using this dependency information initiator can detect and resolve the deadlock cycle. It has been proven in the literature that a deadlock cycle length distribution is skewed with a large majority of cycles (90%) having length two. Based on this fact an improved algorithm is proposed for handling deadlock detection in distributed computing systems under the distributed request model where each process has several requests at a time. The improved algorithm depends mainly on message propagation and timeout policy. Probe and reply messages are propagated between the initiator and its successors only to detect deadlock cycles between them. Global timeout policy is used to detect potential deadlock cycles with large length distribution. The proposed algorithm can handle concurrent executions. Simulation experiments are performed to see the effectiveness of the improved algorithm. It is found that the improved algorithm compares favorably with other existing algorithms and it shows better results for several performance metrics especially in reducing deadlock latency and execution time.

Urban air quality is increasingly becoming a cause for concern for the health of the human population. The poor air quality is already wreaking havoc in major cities of the world, where serious health issues and reduction of average human life by a factor of years are reported. The air quality in developing countries can become worse as they undergo development. The urban air quality varies non-linearly depending upon the various factors such as land use, industrialization, waste disposal, traffic volume, etc. To address this problem, it is necessary to look at the plethora of available literature from multiple perspectives such as types and sources of pollutants, meteorology, urban mobility, urban planning and development, health care, economics, etc. In this paper, we provide a comprehensive survey of the state-of-the-art in urban air quality. We first review the fundamental background on air quality and present the emerging landscape of urban air quality. We then explore the available literature from multiple urban air quality measurement projects and provides the insights uncovered in them. We then take a look at the sources that are significantly contributing to polluting the air quality. Finally, we highlight open issues and research challenges in dealing with urban air pollution.

The complex systems that require safety are the Safety Critical Systems. Maintaining these systems is a big challenge. Now a days, safety is a very critical requirement for the latest systems. Safety critical systems must be safe. Different approaches to ensure quality and safety in safety critical systems has been discussed in this paper. A comparison is also conducted between these various approaches. Safety critical systems must remain more influential in future.

Cyber-Physical Systems form the new backbone of digital ecosystems. Their design can be coupled with engineering activities to facilitate dynamic adaptation and (re-)configuration. Behavior-oriented technologies enable highly distributed and while coupled operation of systems. Utilizing them for digital twins as self-contained design entities with federation capabilities makes them promising candidates to develop and run highly functional CPS. In this paper we discuss mapping CPS components to behavior-based digital twin constituents mirroring integration and implementation through subject-oriented models. These models, inspired by agent-oriented system thinking can be executed and increase transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate configuration of CPS. Subject-oriented runtime support enable dynamic adaptation and federated use.

Blockchain technology can achieve decentralization, multi-party verification, anti-tampering, anonymity, traceability of transactions, and the application of distributed ledger. Countries around the world continue to seek the blockchain business models, technologies and applications, and have different visions and policies for the development of blockchain. This study conducts a comparative policy framework of theoretical analysis of the blockchain technology between the USA and China. Using the innovative policy tools proposed by Rothwell and Zegveld, the above mentioned governments are analyzed from the viewpoint of twelve policy tools. The results show that the USA and China all prefer to use “Environmental-side” policy. The USA has paid more attention to “Legal and regulatory”, “Public services” and “Procurement”. China has the highest proportion of policies in “Political tools”, followed by “Legal & regulatory”, while “Scientific and technical”, “Education” and “Overseas agent” come in third . The blockchain technology has developed vigorously among industries and its applications have gradually diversified. The results are provided to various stakeholders as a reference for policy planning.

Biocultural heritage-based products, including regional specialty foods, are increasingly part of sustainable rural development strategies. While export-oriented biocultural products are often the most visible, we examine the role of campesino gastronomic heritage in the Central Valley of Tarija, Bolivia, as a case study of a local market-centered biocultural resource-based development strategy reflected in an alternative agri-food network. We develop a biocultural sustainability framework to examine this network from ecological, economic and sociocultural perspectives. Data are drawn from interviews (n=77), surveys (n=89) and participant observation, with primary and secondary producers of traditional and new products, as well as restaurant owners, market vendors and local consumers. We find that campesino biocultural heritage and the alternative agri-food network surrounding it represent an influential territorial project that underpins many household economies, particularly for women. We conclude that the relatively small investments by local governments to promote campesino gastronomic heritage are having positive ripple effects on small-scale producer livelihoods and on biocultural sustainability. We suggest that further support to increase market access and reduce other barriers to participation in alternative food networks will likely increase the options and benefits available to small-scale producers mobilising campesino gastronomic heritage within the local economy.

In August 4 2011, United Nations Environmental Program (UNEP) submitted an unprecedented, scientific, groundbreaking Environmental Assessment Report (EAR) of Ogoniland, to the Nigerian government. This was the outcome of a 14–month intensive evaluation of the extent of pollution. It was intended that UNEP’s recommendations would be implemented to restore the devastated environment, on the one hand, and on the other, counteract the numerous environmental health issues that have for decades, plagued Ogoniland. However, five years post EAR, and, despite the seriousness of the situation, no significant resolution has occurred, both on the part of the government, and on the part of Shell Petroleum Development Company (SPDC) or Shell. To date, millions of Niger Delta residents, particularly those living in the oil-bearing communities, continue to suffer severe consequences. Although, the assessment was conducted in Ogoniland, other communities in the Niger Delta are also affected. This article explores prevailing issues, using Ogoniland (a microcosm of the Niger Delta) as an example. A multidisciplinary approach for sustainable mitigation of environmental health risks in the Niger Delta is paramount, and Environmental Management tools offer valuable strategies. Adopting UNEP’s recommendations for addressing environmental health problems requires implementing the Environmental Management/Environmental Management System (EM/EMS) model.

Currently, there are several challenges that Cloud-based health-care Systems, around the world, are facing. The most important issue is to ensure security and privacy or in other words to ensure the confidentiality, integrity and availability of the data. Although the main provisions for data security and privacy were present in the former legal framework for the protection of personal data, the General Data Protection Regulation (GDPR) introduces new concepts and new requirements. In this paper, we present the main changes and the key challenges of the General Data Protection Regulation, and also at the same time we present how the Cloud-based Security Policy methodology proposed in [1] could be modified in order to be compliant with the GDPR and how Cloud environments can assist developers to build secure and GDPR compliant Cloud-based health Systems. The major concept of this paper is, primarily, to facilitate Cloud Providers in comprehending the framework of the new General Data Protection Regulation and secondly, to identify security measures and security policy rules for the protection of sensitive data in a Cloud-based Health System, following our risk-based Security Policy Methodology that assesses the associated security risks and takes into account different requirements from patients, hospitals, and various other professional and organizational actors.

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Machine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The literature review in this article was meticulously conducted with specific criteria in mind. It focused on sourcing peer-reviewed journals, authoritative cybersecurity publications, and recent conferences that primarily concentrate on the advancements in AI and ML within the cybersecurity domain, particularly from 2018 to 2023. This timeframe was chosen to ensure the inclusion of the most current and relevant developments in Open XDR technology, Intrusion Detection Systems, Endpoint Detection and Response, and Security Information and Event Management systems. Additionally, the review gave special attention to studies and reports highlighting practical implementations and real-world applications of these technologies, thereby ensuring a comprehensive understanding of their impact and effectiveness in enhancing cybersecurity resilience. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework.

In this research paper, design and development of pyramidal horn antenna for J-band application is reported. It is particularly designed for 17 dB gain and half beam width about 25 degrees at 6.93 GHz. Horn aperture, horn axial length and distance from the throat of the antenna to aperture are the main design constraints which are calculated and used for the antenna design and simulation. Beam width in E-plane and H-plane horn is calculated and it is 19.18 dB and 22.86 dB respectively. The reported antenna design shows good performance for J-band in radiometry, satellite, and radar applications.

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

Vision impairment may delay responses to hazards when driving. In a proof-of-concept driving simulator study, we evaluated a hazard warning device designed for vision impaired drivers. Three groups participated: 11 persons with central vision loss (CVL; median age 60 years), 12 with homonymous field loss (HFL; 52 years) and 11 with normal vision (NV; 60 years). CVL is characterised by impaired visual acuity and contrast sensitivity while HFL is a loss of vision in the same half of the visual field (blind side) in both eyes. Participants completed drives with and without the device which gave a directional vibro-tactile warning for an approaching pedestrian hazard when collision risk exceeded a pre-defined threshold. Warnings significantly (p< 0.001) reduced brake response times for CVL (∆= 0.54s) and HFL (∆= 0.30s), but not NV participants. For HFL participants, the reduction was greater (p = 0.02) for hazards on the blind (∆= 0.57s) than seeing side (∆= 0.10s). The warnings significantly reduced collision rates for CVL participants from 11 to 0% (all hazards) and for HFL participants from 30 to 1.7% (blind side hazards). Thus, hazard warnings may be beneficial in improving safety of vision impaired drivers; further research is warranted.

Video quality assessment (VQA) plays an important role in video applications for quality evaluation and resource allocation. It aims to evaluate the video quality consistent with the human perception. In this letter, a hierarchical gradient similarity based VQA metric is proposed inspired by the structure of the primate visual cortex, in which visual information is processed through sequential visual areas. These areas are modeled with the corresponding measures to evaluate the overall perceptual quality. Experimental results on the LIVE database show that the proposed VQA metric significantly outperforms the state-of-the-art VQA metrics.

The UK aviation industry is committed to achieving net zero emissions by 2050 through sustainable measures, and one of the key aspects of this effort is the implementation of Unmanned Traffic Management (UTM) systems. These UTM systems play a crucial role in enabling the safe and efficient integration of unmanned aerial vehicles (UAVs) into the airspace. As part of the Airspace of the Future (AoF) project, the development and implementation of UTM services have been prioritized. The paper aims to create an environment where routine drone services can operate safely and effectively. To facilitate this, a digital twin of the National Beyond Visual Line of Sight Experimentation Corridor has been created. This digital twin serves as a virtual replica of the corridor and allows for the synthetic testing of unmanned traffic management concepts. The implementation of the digital twin involves both simulated and hybrid flights with real drones. Simulated flights allow for the testing and refinement of UTM services in a controlled environment. Hybrid flights, on the other hand, involve the integration of real drones into the airspace to assess their performance and compatibility with the UTM systems. By leveraging the capabilities of UTM systems and utilizing the digital twin for testing, the AoF project aims to advance the development of safer and more efficient drone operations. Experimentation Corridor has been developed to simulate and test concepts related to managing unmanned traffic. The paper provides a detailed account of the implementation of the digital twin for the AoF project, including simulated and hybrid flights involving real drones.

The fast-paced evolution of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), large language model (LLM) applications, and generative pre-trained transformer (GPT) have set the stage for improved and autonomous project scheduling models. This study demonstrates how project scheduling can be enhanced by employing building information modeling (BIM) to predict reliable schedules for each project element. The research is based on design science research (DSR) methodology and focus group discussion. The proposed system consists of six components: project analyzer, data warehouse, activity identifier and sequencing, activity magnitude and direct cost calculator, activity duration calculator, and schedule analyzer. Unlike other solutions, the proposed solution sequences activities based on an ML model rather than constraint matrices. The proposed solution thus enhances project performance through proactive planning and risk management. Integrating the autonomous project scheduling solution with the other components of an autonomous project management system would surmount schedule challenges and would result in greater cost efficiency and fewer delays.

In this paper, the power quality of interconnected microgrids is managed using a Model Predictive Control (MPC) methodology which manipulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the microgrids working modes: grid-connected, islanded and interconnected. The results and simulations are also applied to the transition between the different working modes. In order to show the potential of the control algorithm, a comparison study is carried out with classical Proportional-Integral Pulse Width Modulation (PI-PWM) based controllers. The proposed control algorithm not only improves the transient response in comparison with classical methods but also shows an optimal behavior in all the working modes, minimizing the harmonics content in current and voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems

Glioblastoma (GBM), the most common and aggressive primary brain tumor in adults, remains one of the least treatable cancers. Current standard of care—combining surgical resection, radiation, and alkylating chemotherapy—results in a median survival of only 15 months. Despite decades of investment and research into the development of new therapies, most candidate anti-glioma compounds fail to translate into effective treatments in clinical trials. One key issue underlying this failure of therapies that work in pre-clinical models to generate meaningful improvement in human patients is the profound mismatch between drug discovery systems—cell cultures and mouse models—and the actual tumors they are supposed to imitate. Indeed, current strategies that evaluate the effects of novel treatments on GBM cells in vitro fail to account for a wide range of factors known to influence tumor growth. These include secreted factors, the brain’s unique extracellular matrix, circulatory structures, the presence of non-tumor brain cells, and nutrient sources available for tumor metabolism. While mouse models provide a more realistic testing ground for potential therapies, they still fail to account for the full complexity of tumor-microenvironment interactions, as well as the role of the immune system. Based on the limitations of current models, researchers have begun to develop and implement novel culture systems that better recapitulate the complex reality of brain tumors growing in situ. A rise in the use of patient derived cells, creative combinations of added growth factors and supplements, may provide a more effective proving ground for the development of novel therapies. This review will summarize and analyze these exciting developments in 3D culturing systems. Special attention will be paid to how they enhance the design and identification of compounds that increase the efficacy of radiotherapy, a bedrock of GBM treatment.

Aboriginal Australians have a fundamental human right to opportunities that lead to healthy and flourishing lives. While the impact of trauma on Aboriginal Australians is well-documented, a pervasive deficit narrative that focuses on problems and pathology persists in research and policy discourse. This narrative risks further exacerbating Aboriginal disadvantage, through a focus on ‘fixing what is wrong’ with Aboriginal Australians, and the internalising of these narratives by Aboriginal Australians. While a growing body of research adopts strength-based models, limited research has sought to explore Aboriginal flourishing. This conceptual paper seeks to contribute to a burgeoning paradigm shift in Aboriginal research, seeking to understand what can be learned from Aboriginal people who flourish, how we best determine this, and in what contexts this can be impactful. Within, we argue the case for a new approach to exploring Aboriginal wellbeing that integrates salutogenic, positive psychology concepts with complex systems theory to understand and promote Aboriginal wellbeing and flourishing, While deeper work may be required to establish the parameters of a strength-based, culturally aligned Aboriginal conceptualisation of positive psychology, we suggest the integration of Aboriginal and Western methodologies offer unique and potent means of shifting the dial on seemingly intractable problems.

This article presents description of social structures such as, for example, states, in the context of their consideration as systems. Moreover, it seeks to develop K. Marx’s model in terms of systematic approach, as well as to justify the application of the Fibonacci sequence to systems in general and, in particular, to social systems. The description covers a wide range of issues, ranging from the assertion of the basic principle of living systems as patterns in resources flows to description of functions of various aspects of states and their economics in terms of objective needs of such structures. In addition, this article justifies synergetic effect based on proposed model, and the way this effect is applied to state systems. Moreover, it includes the description of main changes of socio-economic formations throughout the history as a natural development of social systems, including possible modern stages. The fundamental tool of this description is description of essence of the mechanism of property rights and its formation options. The differences of two extreme directions of such development are also described on the basis of the objective factor of resource provision. Special attention is given to the concept of surplus value as a feature of social production systems.

The solar heat pump system based on photovoltaic photothermal (PV/T) modules is a new technology that integrates photovoltaics and heat reutilization. It can improve the efficiency of photovoltaic conversion and recovery the remaining heat generated during the photoelectric conversion process, thereby improving the comprehensive efficiency of the whole system. Simulink software is a visual simulation tool, which has the advantages of strong applicability, clear simulation structure and calculation process. The design of its graphical programming greatly simplifies the user modeling process and allows for direct viewing of results, therefore improving the simulation efficiency. At present, there is little research on simulation of solar heat pump systems with PV/T modules based on Simulink software. In this paper, the mathematical models of each component contained in the PV/T solar heat pump system were established based on the system working principle, energy conservation equations and some empirical formulas, and then the operation of the system was simulated by the Simulink software. Due to the operation characteristic of PV/T modules is the key influencing factor for the whole system performance, the model and simulation process for PV/T modules were introduced in detail. The results showed that the surface temperature of PV/T modules under different operating conditions was significantly lower than that of ordinary photovoltaic panels, and the temperature change amplitude was lower. As results, the power generation and photovoltaic conversion efficiency were significantly improved.

Security has become a critical issue in today’s highly distributed and networked systems. Network intrusion detection systems (NIDSs), especially signature-based NIDSs, are being widely deployed in a distributed network environment with the purpose of defending against a variety of network attacks. Most of the commercially available NIDSs are software based and rely on pattern matching to extract the threat from network traffic. The increase in network speed and traffic may make existing algorithms to become a performance bottleneck. Therefore it is very necessary to develop faster and more efficient pattern matching algorithm in order to overcome the troubles on performance of NIDSs. Therefore, we propose a multi fusion pattern matching algorithm for Network Intrusion Detection Systems. The results obtained in percentages from the proposed fusion algorithm given better values in terms processing time in milliseconds than the existing algorithms when data English text are applied to evaluate the fusion performances.

Fifty-eight Karacabey Merino lambs were used to evaluate the effects of feeding system (triticale pasture, oat pasture, or stall fed), birth type (single or multiple), gender (male or female) and birth year (2016 or 2017) on various carcass and meat quality characteristics. Stall lambs had higher conformation (CS) and fatness scores (FS), meat L* and h* values than both pasture groups, possibly as a result of higher fat content, while oat pasture lambs had the most tender meat. Single lambs had higher CS, FS and Longissimus thoracis muscle section area, while females had higher subcutaneous and non-carcass fat depositions, over their counterparts. Both single born lambs and 2016 born lambs had a higher meat a* and C* values than their counterparts. In conclusion, a pasture-based feeding system is thought to be more suitable for Karacabey Merino than stall feeding system, for those who prefer leaner meat and/or meat products. However, it should not be ignored that stall fed lambs showed a better fattening performance in terms of conformation and fatness, in the same period.

: The article presents a brief review of cooling systems of various temperature levels (from 0.1 K to 230 K) for radio astronomical receivers of photonics and electronics. The features of various levels of cooling and the requirements for the design of the cooling system are considered in detail, as well as approaches to designing interfaces for cooled receivers: vacuum, cryogenic, electrical, mechanical, optical, and others required for the effective operation of the receiver,. The presented approaches to design are illustrated by a series of joint developments of the authors carried out over the past 45 years, including those produced last year.

The study aims to examine the effects of artificial intelligence (AI) on the consistency and analysis of financial statements in hotels in ASEZA, Jordan. This research is an exploratory, empirical study, which uses the methodology of data collection and interpretation to draw conclusions. The researchers used the arithmetic mean, standard deviation, T-test and ANOVA test to calculate the degree of significance of the study questions. The findings of a basic linear regression study of the impact of AI implemented in Jordanian hotels on the integration of accounting information systems and the association between AI and the integration of accounting information systems (R = 59.6%) also indicate that the fixed limit value amounted to (2.060) and the value of (Beta) for T-test

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. Static analysis (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the dynamic analysis, based on five coarse-grained descriptors related to variability, degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature to shift from a metaphorical to a fully quantitative status.

A review of desiccant dehumidification technologies for improving air-quality has been presented, especially focusing on alternatives for air conditioning systems for minimizing the Sick Building Syndrome. The principles and types of desiccant wheels, as well as the existing selection software for these types of equipment, were reviewed and comparatively evaluated. The study focuses on the Brazilian context, therefore, information about air condition systems and laws of this country were evaluated. Possible applications of desiccant wheels are also analyzed, such as their integration into cooling cycles and the sensible heat wheel. Finally, several commercial desiccant wheel selection software were evaluated, which are useful in many situations. Nevertheless, it was evidenced that the available softwares are not capable of performing an operation analysis for only a specific period. Therefore, it is essential to create computational tools to select desiccant wheels, considering the data from the different Brazilian regions for a year.

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

INTRODUCTION: With an increase in the aging population, the application of evidence-based practice in geriatric education can strengthen knowledge, skills, and clinical experience for healthcare students caring for older adults. Age Friendly Health Systems (AFHS) is one of the frameworks founded on providing evidence-based and low-risk care centered on what matters most to older adults, their families, and caregivers. Virtual Reality (VR) platforms are gaining popularity due to their ability to provide an immersive, hands-on learning experience resembling an actual medical practice or care setting. Immersive learning enhances students’ sensory perceptions, promoting an innovative and engaging way of acquiring concepts that are difficult to teach in real life. This study aimed to design, develop, implement, and evaluate a case-based training module highlighting AFHS and educating medical students on the 4Ms approach in geriatric care (What Matters, Medication, Mentation, and Mobility). METHODOLOGY: The project was a feasibility study completed in two phases. Phase one included planning and development of a case-based scenario incorporating 4Ms of AFHS. Phase two included implementing and evaluating the VR training module into the geriatric curriculum for medical students. RESULTS: The final VR case displays a hospital and post-acute setting where an elderly patient is admitted for a hip fracture. Students learn how to triage and treat patients from admission to discharge while demonstrating their knowledge of AFHS. Approximately 10% of students completed the evaluation survey, and preliminary results indicate significant knowledge change in pre-post scenario-based training on AFHS. CONCLUSION: The VR education platform and embedded scenario promise an innovative adaptation of technology in learning the concepts of the 4Ms of AFHS. However, future studies should explore VR education with clinical assessment evaluation to ensure competence in providing Age-Friendly care.

Fresh-cut apple is commonly known as minimally processed agriculture products because of their convenience and ease of consumption. However, during storage, the quality of the apple rapidly changes after the cutting process due to enzymatic and non-enzymatic processes. This study aimed to monitor the quality changes of fresh-cut apples at various temperatures and different types of products in the application of a cold chain system during distribution and storage. The results showed that the total viable count in fresh-cut apples initially differed between apple only and mixed products (apple, orange, and tomato) during storage (p &lt; 0.05). Moreover, there are no significant differences (p &gt; 0.05) in physicochemicals except firmness, titratable acidity, and pH, which are highly correlated to microbial growth (total viable count and total coliform number). Furthermore, the optimal temperature for extending the shelf-life of fresh-cut apples was at 4°C.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

The urgency to develop vaccines against Covid-19 is putting pressure on the long and expensive development timelines which are normally required for development of lifesaving vaccines. There is a unique opportunity to take advantage of new technologies, smart and flexible design of clinical trials, and evolving regulatory science to speed up vaccine development against Covid-19 and transform vaccine development altogether.

The catastrophic failure of two dams in Libya in the night of 10th and 11th September 2023, resulted in a significant amount of water and debris destroying a large part of the city of Derna, which is located downstream of the two dams. The flood caused more than 6000 fatalities and the dis-placement of thousands of people from the city. The dams failed due to heavy rainfall caused by the Medicane Daniel, resulting in intense precipitation and the dams overspilling after reaching full capacity. This paper analyses the consequences of the dam breach by modelling the flow discharge and the flooding of the city of Derna. The analysis compares the computed quantities with the findings of the post-event satellite image analysis from UNOSAT and Copernicus, the field reports, and information gathered from the social media through AI detection. The results allows to better understand the dynamics of the dam breach, its initial parameters and an assessment of the accu-racy of the results.

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Fruit production is an important part of the gross domestic product for many countries around the world especially to those who have a strong focus on agriculture. However, long-term maintenance and yield stability of fruit production may be threatened by the ongoing climate change and its consequences like extended drought periods, heavy rain events, and floodings. Genome editing, with its progressive technological developments, offers opportunities to adapt relevant fruit plant species to new climatic conditions. Among modern genome editing techniques, CRISPR/Cas, in particular, has the potential to support breeding for those fruit plant species with extended breeding cycles, e.g., perennial fruits. In this review, we discuss CRISPR/Cas and other genome editing techniques in detail and how these techniques can be applied to support the breeding of fruit plant species for adaptation to changing climates. The chronological history of CRISPR/Cas9 systems, their associated computational tools, genomic data sources, transformation methods along with their delivery vehicles, quality improvement, environmental-stress resiliency, limitations, and future perspectives will also be discussed with respect to securing future global fruit production.

This study examines the capacity of a Site-Specific Fodder Management Decision Support System (SSFM-DSS) model to assist in successful cultivation of sericea lespedeza [SL; Lespedeza cuneata (Dum.-Cours.) G. Don.], with a specific focus on small-scale agricultural systems in the southeastern United States (U.S.). The study emphasizes incorporating advanced geospatial technologies, such as Geographic Information Systems (GIS), remote sensing, and Global Navigation Satellite Systems (GNSS), to improve fodder production. The research showcases the versatility of SL under varying environmental conditions, underscoring its significance in promoting sustainable livestock production. The methodology integrates empirical field data, geospatial analysis, and predictive modeling to formulate an SSFM strategy customized to address the distinct difficulties climate change presents, such as sudden and severe drought conditions. The study introduces an automated geospatial model for assessing the appropriateness of SL production throughout Alabama, Georgia, and South Carolina. The model uses many environmental criteria, including soil properties, climate fluctuations, and terrain. Furthermore, the research was instrumental in development of a WebGIS Dashboard, which offers farmers a decision assistance system to enhance the sustainable production of SL. The results highlight the significant impact that a SSFM-DSS can have on promoting sustainable agricultural practices, providing a solution to address the issues of food security and environmental concerns in agriculture.

This paper describes a case study for applying of hybrid-electric propulsion system for a general aviation aircraft. The work was performed by a joint team of CIRA and the Department of Industrial Engineering of the University of Naples “Federico II”. Electric and hybrid electric propulsion for aircraft has gained widespread and significant attention over the past decade. The driver for industry interest has principally been the need to reduce emissions of combustion engine exhaust products and noise, but increasingly studies revealed potential for overall improvement in energy efficiency and mission flexibility of new aircraft types. The project goal was to demonstrate feasibility of aeronautic parallel hybrid-electric propulsion for a Light aircraft varying the mission profiles and the electric configuration. Through a creation, and application, of a global model, with software AMESim^®, in which it can be represented everything about the components chosen by the industrial partners, some interesting considerations are carried out. In particular, it was confirmed that with the only integration of state of the art technologies, for some particular missions, the advantages of aircraft hybrid-electric propulsion, for light aircraft, are notable.

The controls of depositional environments on reservoir quality have been evaluated in terms of porosity and permeability of the Gabo Field, Niger Delta, Nigeria. Data used in this research include Well logs, Core data and photos, and grain size analysis for Wells 51 and 52 in the study area. Standard methods as applicable in petrophysical and sedimentological analysis has been adopted. Thirteen reservoir units have been identified in wells 51 and 52 which had 5 reservoirs cored each. The lithofacies units of the identified reservoirs across the study area, comprise pebbly sands, coarse -, medium -, fine- and very fine-grained sands, sandy mud, silty sands and heteroliths. The heteroliths – very fine-grained silty muds are highly bioturbated. Ophiomorpha and skolithos are the major trace fossils with sedimentary structures (ripple lamination, wavy lenticular and planar beds, cross bedded sands, coarsening and fining upward). The facies associations interpreted for the study area are Channel and Coastal barrier systems and the environment of deposition as distributary channel, upper and lower shoreface. The sedimentary processes that deposited facies ranged from high energy regimes, reworking by waves to low energy with periodic influx of silts and muds. The average porosity and permeability for reservoirs in Well 51 is 16.7% and 1317 Md, reservoirs in Well 52 is 28.2% and 2330Md whereas porosity range for the study area is 2% - 32% and permeability is 1.2 – 10600 Md. The reservoir quality reservoir of the sand units in Well 51 (7, 9 and 13) and Well 52 (5, 7, 9, 11 and 13) is excellent - good, this is because of the dynamics environments of deposition (upper shoreface and distributary channel) as well as the mechanisms that play out during deposition such as bioturbation, sorting, sedimentary structures formed. Whereas the poor quality across the reservoirs especially the lower shoreface and prodelta facies is as result of lack bioturbation, connectivity, multiplicity of burrows that may have been plugged by clay and intercalation of shale and sand (heteroliths). This research has shown that environments of deposition have direct influence the reservoir quality in terms of porosity and permeability.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

In this paper the development process of a deployable modular sandwich panelized system for rapid assembly building construction is presented, and its structural performance under some different action effects is investigated. This system, which includes an innovative sandwich panel and its integrated connections, can be used as structural walls and floors in quickly assembled post-disaster housing, as well as load bearing panels for pre-fabricated modular construction and semi-permanent buildings. Panels and connections are composed of a pneumatic fabric formwork, and two 3-D high-density polyethylene (HDPE) sheets as the skins, filled with high-density rigid Polyurethane (PU) foam as the core. HDPE sheets manufactured with a studded surface considerably enhance the stress distribution, buckling performance and delamination strength of the sandwich panel under various loading conditions. The load-carrying behaviour of the system in accordance with some ASTM standards is presented here. The results show the system satisfies the codes criteria regarding semi-permanent housing.

Artificial Intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus AI-based modeling is the key to building automated, intelligent, and smart systems according to today's needs. To solve real-world issues various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on "AI-based Modeling" with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

The Sustainable Development Goals, while complex at first sight, express a simple narrative about the relationships between people and nature. This paper illustrates this in the context of a coral reef land or seascape supporting coastal people. Coral reefs, their health described by measures of coral and fish diversity and abundance, provide key services and benefits to people. These services directly support 10s of millions of jobs in multiple economic sectors in coastal and distant states, protect and harbor communities and cities across tropical coastlines, sustain use of living and non-living resources, provide transport infrastructure and valuable natural products, and in future may provide energy solutions. Through these multiple benefits, coral reefs contribute to reducing hunger and poverty, thus improving health, and potentially strengthening gender and social equality. However, access and use result in pressures that may drive decline in coral reef health. Broader land and seascape factors also affect reef health and therefore delivery of benefits, including land-use change and altered freshwater flows, as well as climate change. Managing this complex system requires appropriate awareness and knowledge, governance mechanisms and investments by stakeholders. This ‘SDG narrative’ can be used from local to global levels, motivating actions and policy at and across these scales to sustain ecosystem function and use, for the oceans what is also increasingly called a blue economy.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

(1) Background: Due to the high proportion of disadvantaged students in a rural school in Taiwan and the gap between students’ concepts and practices of environmental protection and sustainable energy, four science and mathematics teachers in this school planned an engineering-centered PjBL of sustainable energy curriculum in a Makers Club to enhance students’ creativity, engineering skills, practices of environmental protection and sustainable energy, and learning attitudes; (2) Methods: This study is four-year action research. Teachers and students initiated the idea from rebuilding an old fan in a classroom; (3) Results: The students in the Makers Club improved their engineering skills and created various green-power generation devices (evolved from ventilation ball generator, hydropower, ocean current power generators to tiny, 3D-printing wind power generators). They turned environmental protection and sustainable energy concepts into actions during practices and won awards from science and engineering fairs every year. This creative and supportive atmosphere spread from the club to the whole school and improved the students’ practices of environmental protection and learning attitudes after long-term implementation; (4) Conclusions: The design principles of the engineering-centered PjBL of sustainable energy curriculum played a critical role and were outlined at the end of the study.

We are all similar, but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly and yeast panels with insightful applications for translational research.